Abstract 4524: Multiparametric characterization of early-stage SCLC human tumors reveals novel patient subgroups based on specific molecular to immune landscape associations
Authors: Angel Nunez-Buiza, David Gómez-Sánchez, Jose Maria Gracia-Rodríguez, Esther Conde, Jose Luis Solorzano, Eva María Garrido-Martín, Jose Carlos Machado, Susana Guimarães, Ernest Nadal, Sonia Molina-Pinelo, Miguel Ángel Piris Pinilla, Nuria Romero-Laorden, Fernando Franco, Fernando López-Ríos Moreno, Álvaro Conrado Ucero, Luis Paz-Ares
Journal: Cancer Research (2023)

Abstract Small cell lung cancer (SCLC) is a lethal malignancy with few therapeutic options. Tissue resection or biopsy are unusual, so the scarce sample available has limited our knowledge on the biology of SCLC tumors. Recent advances propose novel molecular subtypes (SCLC-A, SCLC-N, SCLC-P, SCLC-I, NAPI classification) to classify SCLC patients and specific treatment susceptibilities of defined subgroups. In this line, we collected 119 tumor samples from a cohort of early-stage SCLC patients with complete clinical annotation, in order to find specific molecular and immune profiles that define new patient characteristics with potential impact on prognosis or therapy selection. For this, we subjected the tumor tissue to RNA-seq targeted panel of onco-immune-related genes and to immune characterization by IHC. In addition, we analyzed molecular aberrations by whole exome sequencing. All data, including clinical annotations, were integrated by multiparametric computational analysis. Based on the computational analysis of the onco-immune transcriptomic data, we found two major groups of patients with either pro-immunogenic or pro-tumorigenic profiles. The transcriptomic pro-immunogenic group shows better survival and higher infiltration of immune cells (CD4+ T cells, CD8+ T cells, B cells and Macrophages). In contrast, the pro-tumorigenic group presents worse survival and less immune cell infiltration. Using bioinformatics, we described a gene signature that can identify both subgroups. This gene signature includes immune cell markers (MS4A1, CD3D), antigen receptor complex (CD79A), adhesion and migration of T cells (CD2) and immunomodulatory genes (IDO1, TIGIT) expressed in the pro-immunogenic group, and, proliferation (MKI67), transcription regulator (TOP2A) and epithelial and mesenchymal transition-related genes (TWIST1) in the pro-tumorigenic group. Exome sequencing analysis resulted in the expected genomic heterogeneity of SCLC tumors beyond TP53 and RB1 mutations in most patients. However, we found a significant enrichment of XIRP2 gene alterations in the pro-tumorigenic group. Consistent with an early-stage of SCLC, most of the samples were described as SCLC-A subtype by the predominant expression of ASCL1. Interestingly, our findings suggest a relevant biological heterogeneity within SCLC-A tumors that impacts immune infiltrate and disease outcome. Here we show a gene signature that can subclassify early-stage SCLC patients according to their clinical, molecular and immune features, and provides prognostic value independently of the NAPI classification. Since immunogenicity of the tumor impacts response to immunotherapy, we speculate that this gene signature might predict therapy response and therefore contribute to tailored treatment of SCLC. Citation Format: Angel Nunez-Buiza, David Gómez-Sánchez, Jose Maria Gracia-Rodríguez, Esther Conde, Jose Luis Solorzano, Eva María Garrido-Martín, Jose Carlos Machado, Susana Guimarães, Ernest Nadal, Sonia Molina-Pinelo, Miguel Ángel Piris Pinilla, Nuria Romero-Laorden, Fernando Franco, Fernando López-Ríos Moreno, Álvaro Conrado Ucero, Luis Paz-Ares. Multiparametric characterization of early-stage SCLC human tumors reveals novel patient subgroups based on specific molecular to immune landscape associations. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4524.